{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('1584102211.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SaleID</th>\n",
       "      <th>name</th>\n",
       "      <th>regDate</th>\n",
       "      <th>model</th>\n",
       "      <th>brand</th>\n",
       "      <th>bodyType</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>...</th>\n",
       "      <th>v_5</th>\n",
       "      <th>v_6</th>\n",
       "      <th>v_7</th>\n",
       "      <th>v_8</th>\n",
       "      <th>v_9</th>\n",
       "      <th>v_10</th>\n",
       "      <th>v_11</th>\n",
       "      <th>v_12</th>\n",
       "      <th>v_13</th>\n",
       "      <th>v_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>736</td>\n",
       "      <td>20040402</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235676</td>\n",
       "      <td>0.101988</td>\n",
       "      <td>0.129549</td>\n",
       "      <td>0.022816</td>\n",
       "      <td>0.097462</td>\n",
       "      <td>-2.881803</td>\n",
       "      <td>2.804097</td>\n",
       "      <td>-2.420821</td>\n",
       "      <td>0.795292</td>\n",
       "      <td>0.914762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2262</td>\n",
       "      <td>20030301</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264777</td>\n",
       "      <td>0.121004</td>\n",
       "      <td>0.135731</td>\n",
       "      <td>0.026597</td>\n",
       "      <td>0.020582</td>\n",
       "      <td>-4.900482</td>\n",
       "      <td>2.096338</td>\n",
       "      <td>-1.030483</td>\n",
       "      <td>-1.722674</td>\n",
       "      <td>0.245522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SaleID  name   regDate  model  brand  bodyType  fuelType  gearbox  power  \\\n",
       "0       0   736  20040402   30.0      6       1.0       0.0      0.0     60   \n",
       "1       1  2262  20030301   40.0      1       2.0       0.0      0.0      0   \n",
       "\n",
       "   kilometer  ...       v_5       v_6       v_7       v_8       v_9      v_10  \\\n",
       "0       12.5  ...  0.235676  0.101988  0.129549  0.022816  0.097462 -2.881803   \n",
       "1       15.0  ...  0.264777  0.121004  0.135731  0.026597  0.020582 -4.900482   \n",
       "\n",
       "       v_11      v_12      v_13      v_14  \n",
       "0  2.804097 -2.420821  0.795292  0.914762  \n",
       "1  2.096338 -1.030483 -1.722674  0.245522  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('data/used_car_train_20200313.csv', sep=' ')\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SaleID</th>\n",
       "      <th>name</th>\n",
       "      <th>regDate</th>\n",
       "      <th>model</th>\n",
       "      <th>brand</th>\n",
       "      <th>bodyType</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>...</th>\n",
       "      <th>v_5</th>\n",
       "      <th>v_6</th>\n",
       "      <th>v_7</th>\n",
       "      <th>v_8</th>\n",
       "      <th>v_9</th>\n",
       "      <th>v_10</th>\n",
       "      <th>v_11</th>\n",
       "      <th>v_12</th>\n",
       "      <th>v_13</th>\n",
       "      <th>v_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150000</td>\n",
       "      <td>66932</td>\n",
       "      <td>20111212</td>\n",
       "      <td>222.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>313</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.264405</td>\n",
       "      <td>0.1218</td>\n",
       "      <td>0.070899</td>\n",
       "      <td>0.106558</td>\n",
       "      <td>0.078867</td>\n",
       "      <td>-7.050969</td>\n",
       "      <td>-0.854626</td>\n",
       "      <td>4.800151</td>\n",
       "      <td>0.620011</td>\n",
       "      <td>-3.664654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150001</td>\n",
       "      <td>174960</td>\n",
       "      <td>19990211</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>12.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261745</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.096733</td>\n",
       "      <td>0.013705</td>\n",
       "      <td>0.052383</td>\n",
       "      <td>3.679418</td>\n",
       "      <td>-0.729039</td>\n",
       "      <td>-3.796107</td>\n",
       "      <td>-1.541230</td>\n",
       "      <td>-0.757055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SaleID    name   regDate  model  brand  bodyType  fuelType  gearbox  power  \\\n",
       "0  150000   66932  20111212  222.0      4       5.0       1.0      1.0    313   \n",
       "1  150001  174960  19990211   19.0     21       0.0       0.0      0.0     75   \n",
       "\n",
       "   kilometer  ...       v_5     v_6       v_7       v_8       v_9      v_10  \\\n",
       "0       15.0  ...  0.264405  0.1218  0.070899  0.106558  0.078867 -7.050969   \n",
       "1       12.5  ...  0.261745  0.0000  0.096733  0.013705  0.052383  3.679418   \n",
       "\n",
       "       v_11      v_12      v_13      v_14  \n",
       "0 -0.854626  4.800151  0.620011 -3.664654  \n",
       "1 -0.729039 -3.796107 -1.541230 -0.757055  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('data/used_car_testA_20200313.csv', sep=' ')\n",
    "test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((150000, 31), (50000, 30))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看下数据集样本数\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索性数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值检查"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuelType    8680\n",
       "gearbox     5981\n",
       "bodyType    4506\n",
       "model          1\n",
       "v_14           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求空值数量\n",
    "train.isnull().sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb2d856ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标值分布，右偏分布\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "sns.set_style(\"white\")\n",
    "sns.distplot(train['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SaleID</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>7.500444e+04</td>\n",
       "      <td>43303.903862</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.749925e+04</td>\n",
       "      <td>7.501350e+04</td>\n",
       "      <td>1.125108e+05</td>\n",
       "      <td>1.499990e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>6.830758e+04</td>\n",
       "      <td>61096.691549</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.114000e+04</td>\n",
       "      <td>5.157950e+04</td>\n",
       "      <td>1.187758e+05</td>\n",
       "      <td>1.968120e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regDate</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>2.003421e+07</td>\n",
       "      <td>53625.492917</td>\n",
       "      <td>1.991000e+07</td>\n",
       "      <td>1.999100e+07</td>\n",
       "      <td>2.003091e+07</td>\n",
       "      <td>2.007111e+07</td>\n",
       "      <td>2.015121e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>149765.0</td>\n",
       "      <td>4.714586e+01</td>\n",
       "      <td>49.544684</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>6.600000e+01</td>\n",
       "      <td>2.470000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>8.051647e+00</td>\n",
       "      <td>7.863853</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>3.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bodyType</th>\n",
       "      <td>145381.0</td>\n",
       "      <td>1.792641e+00</td>\n",
       "      <td>1.760770</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuelType</th>\n",
       "      <td>141216.0</td>\n",
       "      <td>3.759985e-01</td>\n",
       "      <td>0.548628</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gearbox</th>\n",
       "      <td>143920.0</td>\n",
       "      <td>2.250625e-01</td>\n",
       "      <td>0.417625</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>1.194570e+02</td>\n",
       "      <td>177.259546</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.500000e+01</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.500000e+02</td>\n",
       "      <td>1.931200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kilometer</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>1.260269e+01</td>\n",
       "      <td>3.911367</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>1.250000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "      <td>1.500000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>regionCode</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>2.582548e+03</td>\n",
       "      <td>1885.112390</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.018000e+03</td>\n",
       "      <td>2.196000e+03</td>\n",
       "      <td>3.842000e+03</td>\n",
       "      <td>8.120000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seller</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>6.677083e-06</td>\n",
       "      <td>0.002584</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offerType</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatDate</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>2.016033e+07</td>\n",
       "      <td>106.808445</td>\n",
       "      <td>2.015062e+07</td>\n",
       "      <td>2.016031e+07</td>\n",
       "      <td>2.016032e+07</td>\n",
       "      <td>2.016033e+07</td>\n",
       "      <td>2.016041e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>8.042166e+00</td>\n",
       "      <td>1.206489</td>\n",
       "      <td>4.025352e+00</td>\n",
       "      <td>7.170888e+00</td>\n",
       "      <td>8.098947e+00</td>\n",
       "      <td>8.949105e+00</td>\n",
       "      <td>1.151293e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_0</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>4.441813e+01</td>\n",
       "      <td>2.436506</td>\n",
       "      <td>3.045198e+01</td>\n",
       "      <td>4.314386e+01</td>\n",
       "      <td>4.461449e+01</td>\n",
       "      <td>4.600738e+01</td>\n",
       "      <td>5.230418e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_1</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>-4.077136e-02</td>\n",
       "      <td>3.642030</td>\n",
       "      <td>-4.200244e+00</td>\n",
       "      <td>-3.191876e+00</td>\n",
       "      <td>-3.052135e+00</td>\n",
       "      <td>4.002139e+00</td>\n",
       "      <td>7.320308e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_2</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>7.029819e-02</td>\n",
       "      <td>2.900404</td>\n",
       "      <td>-4.470671e+00</td>\n",
       "      <td>-9.698108e-01</td>\n",
       "      <td>-3.829775e-01</td>\n",
       "      <td>2.401814e-01</td>\n",
       "      <td>1.903550e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_3</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>6.861716e-02</td>\n",
       "      <td>2.010744</td>\n",
       "      <td>-7.275037e+00</td>\n",
       "      <td>-1.464531e+00</td>\n",
       "      <td>9.495306e-02</td>\n",
       "      <td>1.559500e+00</td>\n",
       "      <td>7.351431e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_4</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>1.614979e-02</td>\n",
       "      <td>1.193206</td>\n",
       "      <td>-4.364565e+00</td>\n",
       "      <td>-9.223183e-01</td>\n",
       "      <td>-7.843434e-02</td>\n",
       "      <td>8.664501e-01</td>\n",
       "      <td>6.829352e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_5</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>2.484127e-01</td>\n",
       "      <td>0.045291</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.436874e-01</td>\n",
       "      <td>2.578326e-01</td>\n",
       "      <td>2.653074e-01</td>\n",
       "      <td>2.918381e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_6</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>4.497238e-02</td>\n",
       "      <td>0.051752</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.110095e-05</td>\n",
       "      <td>8.143635e-04</td>\n",
       "      <td>1.020439e-01</td>\n",
       "      <td>1.514196e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_7</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>1.238176e-01</td>\n",
       "      <td>0.199079</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.242337e-02</td>\n",
       "      <td>9.582249e-02</td>\n",
       "      <td>1.251777e-01</td>\n",
       "      <td>1.404936e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_8</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>5.822959e-02</td>\n",
       "      <td>0.029126</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.544100e-02</td>\n",
       "      <td>5.707530e-02</td>\n",
       "      <td>7.942255e-02</td>\n",
       "      <td>1.607910e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_9</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>6.196420e-02</td>\n",
       "      <td>0.035678</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.391571e-02</td>\n",
       "      <td>5.845268e-02</td>\n",
       "      <td>8.746072e-02</td>\n",
       "      <td>2.227875e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_10</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>-1.264251e-02</td>\n",
       "      <td>3.760128</td>\n",
       "      <td>-9.168192e+00</td>\n",
       "      <td>-3.726025e+00</td>\n",
       "      <td>1.618495e+00</td>\n",
       "      <td>2.840117e+00</td>\n",
       "      <td>1.172691e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_11</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>-5.409817e-03</td>\n",
       "      <td>3.253549</td>\n",
       "      <td>-5.558207e+00</td>\n",
       "      <td>-1.953767e+00</td>\n",
       "      <td>-3.632335e-01</td>\n",
       "      <td>1.250444e+00</td>\n",
       "      <td>1.881904e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_12</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>9.501503e-03</td>\n",
       "      <td>2.512218</td>\n",
       "      <td>-7.591009e+00</td>\n",
       "      <td>-1.869418e+00</td>\n",
       "      <td>-1.289642e-01</td>\n",
       "      <td>1.778743e+00</td>\n",
       "      <td>1.384779e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_13</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>1.158212e-03</td>\n",
       "      <td>1.289126</td>\n",
       "      <td>-4.153899e+00</td>\n",
       "      <td>-1.057122e+00</td>\n",
       "      <td>-3.468294e-02</td>\n",
       "      <td>9.436265e-01</td>\n",
       "      <td>1.114767e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_14</th>\n",
       "      <td>149766.0</td>\n",
       "      <td>4.542389e-04</td>\n",
       "      <td>1.038787</td>\n",
       "      <td>-6.546556e+00</td>\n",
       "      <td>-4.351074e-01</td>\n",
       "      <td>1.424816e-01</td>\n",
       "      <td>6.813142e-01</td>\n",
       "      <td>8.658418e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count          mean           std           min           25%  \\\n",
       "SaleID      149766.0  7.500444e+04  43303.903862  0.000000e+00  3.749925e+04   \n",
       "name        149766.0  6.830758e+04  61096.691549  0.000000e+00  1.114000e+04   \n",
       "regDate     149766.0  2.003421e+07  53625.492917  1.991000e+07  1.999100e+07   \n",
       "model       149765.0  4.714586e+01     49.544684  0.000000e+00  1.000000e+01   \n",
       "brand       149766.0  8.051647e+00      7.863853  0.000000e+00  1.000000e+00   \n",
       "bodyType    145381.0  1.792641e+00      1.760770  0.000000e+00  0.000000e+00   \n",
       "fuelType    141216.0  3.759985e-01      0.548628  0.000000e+00  0.000000e+00   \n",
       "gearbox     143920.0  2.250625e-01      0.417625  0.000000e+00  0.000000e+00   \n",
       "power       149766.0  1.194570e+02    177.259546  0.000000e+00  7.500000e+01   \n",
       "kilometer   149766.0  1.260269e+01      3.911367  5.000000e-01  1.250000e+01   \n",
       "regionCode  149766.0  2.582548e+03   1885.112390  0.000000e+00  1.018000e+03   \n",
       "seller      149766.0  6.677083e-06      0.002584  0.000000e+00  0.000000e+00   \n",
       "offerType   149766.0  0.000000e+00      0.000000  0.000000e+00  0.000000e+00   \n",
       "creatDate   149766.0  2.016033e+07    106.808445  2.015062e+07  2.016031e+07   \n",
       "price       149766.0  8.042166e+00      1.206489  4.025352e+00  7.170888e+00   \n",
       "v_0         149766.0  4.441813e+01      2.436506  3.045198e+01  4.314386e+01   \n",
       "v_1         149766.0 -4.077136e-02      3.642030 -4.200244e+00 -3.191876e+00   \n",
       "v_2         149766.0  7.029819e-02      2.900404 -4.470671e+00 -9.698108e-01   \n",
       "v_3         149766.0  6.861716e-02      2.010744 -7.275037e+00 -1.464531e+00   \n",
       "v_4         149766.0  1.614979e-02      1.193206 -4.364565e+00 -9.223183e-01   \n",
       "v_5         149766.0  2.484127e-01      0.045291  0.000000e+00  2.436874e-01   \n",
       "v_6         149766.0  4.497238e-02      0.051752  0.000000e+00  4.110095e-05   \n",
       "v_7         149766.0  1.238176e-01      0.199079  0.000000e+00  6.242337e-02   \n",
       "v_8         149766.0  5.822959e-02      0.029126  0.000000e+00  3.544100e-02   \n",
       "v_9         149766.0  6.196420e-02      0.035678  0.000000e+00  3.391571e-02   \n",
       "v_10        149766.0 -1.264251e-02      3.760128 -9.168192e+00 -3.726025e+00   \n",
       "v_11        149766.0 -5.409817e-03      3.253549 -5.558207e+00 -1.953767e+00   \n",
       "v_12        149766.0  9.501503e-03      2.512218 -7.591009e+00 -1.869418e+00   \n",
       "v_13        149766.0  1.158212e-03      1.289126 -4.153899e+00 -1.057122e+00   \n",
       "v_14        149766.0  4.542389e-04      1.038787 -6.546556e+00 -4.351074e-01   \n",
       "\n",
       "                     50%           75%           max  \n",
       "SaleID      7.501350e+04  1.125108e+05  1.499990e+05  \n",
       "name        5.157950e+04  1.187758e+05  1.968120e+05  \n",
       "regDate     2.003091e+07  2.007111e+07  2.015121e+07  \n",
       "model       3.000000e+01  6.600000e+01  2.470000e+02  \n",
       "brand       6.000000e+00  1.300000e+01  3.900000e+01  \n",
       "bodyType    1.000000e+00  3.000000e+00  7.000000e+00  \n",
       "fuelType    0.000000e+00  1.000000e+00  6.000000e+00  \n",
       "gearbox     0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "power       1.100000e+02  1.500000e+02  1.931200e+04  \n",
       "kilometer   1.500000e+01  1.500000e+01  1.500000e+01  \n",
       "regionCode  2.196000e+03  3.842000e+03  8.120000e+03  \n",
       "seller      0.000000e+00  0.000000e+00  1.000000e+00  \n",
       "offerType   0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "creatDate   2.016032e+07  2.016033e+07  2.016041e+07  \n",
       "price       8.098947e+00  8.949105e+00  1.151293e+01  \n",
       "v_0         4.461449e+01  4.600738e+01  5.230418e+01  \n",
       "v_1        -3.052135e+00  4.002139e+00  7.320308e+00  \n",
       "v_2        -3.829775e-01  2.401814e-01  1.903550e+01  \n",
       "v_3         9.495306e-02  1.559500e+00  7.351431e+00  \n",
       "v_4        -7.843434e-02  8.664501e-01  6.829352e+00  \n",
       "v_5         2.578326e-01  2.653074e-01  2.918381e-01  \n",
       "v_6         8.143635e-04  1.020439e-01  1.514196e-01  \n",
       "v_7         9.582249e-02  1.251777e-01  1.404936e+00  \n",
       "v_8         5.707530e-02  7.942255e-02  1.607910e-01  \n",
       "v_9         5.845268e-02  8.746072e-02  2.227875e-01  \n",
       "v_10        1.618495e+00  2.840117e+00  1.172691e+01  \n",
       "v_11       -3.632335e-01  1.250444e+00  1.881904e+01  \n",
       "v_12       -1.289642e-01  1.778743e+00  1.384779e+01  \n",
       "v_13       -3.468294e-02  9.436265e-01  1.114767e+01  \n",
       "v_14        1.424816e-01  6.813142e-01  8.658418e+00  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 99662\n",
      "model 248\n",
      "brand 40\n",
      "regionCode 7905\n"
     ]
    }
   ],
   "source": [
    "# 查看分类特征的类别数量\n",
    "for i in ['name', 'model', 'brand', 'regionCode']:\n",
    "    print(i, train[i].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb2d9c6128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看特征相关系数\n",
    "corr = train.corr()\n",
    "plt.subplots(figsize=(10, 8))\n",
    "sns.heatmap(corr, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理倾斜特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 目标值做log处理\n",
    "train['price'] = np.log1p(train['price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb2db6b550>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看转化后的分布，有点正态的感觉了\n",
    "fig, ax = plt.subplots(figsize=(8, 7))\n",
    "sns.distplot(train['price'], fit=norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 移除异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以根据计算结果或者其他特征进行移除\n",
    "train.drop(train[train['price'] < 4].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 整合训练集测试集以便后续特征工程\n",
    "train_labels = train['price'].reset_index(drop=True)\n",
    "train_features = train.drop(['price'], axis=1)\n",
    "test_features = test\n",
    "all_features = pd.concat([train_features, test_features]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据最常出现填充，都是零，也可以根据其他来填充，不是瞎填的。。\n",
    "def fill_missing(df):\n",
    "    df['fuelType'] = df['fuelType'].fillna(0)\n",
    "    df['gearbox'] = df['gearbox'].fillna(0)\n",
    "    df['bodyType'] = df['bodyType'].fillna(0)\n",
    "    df['model'] = df['model'].fillna(0)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = fill_missing(all_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SaleID     0\n",
       "name       0\n",
       "regDate    0\n",
       "model      0\n",
       "brand      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features.isnull().sum().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据类型转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理完\n",
    "def data_astype(df):\n",
    "    # string\n",
    "    df['SaleID'] = df['SaleID'].astype(int).astype(str)\n",
    "    df['name'] = df['name'].astype(int).astype(str)\n",
    "    df['model'] = df['model'].astype(str)\n",
    "    df['brand'] = df['brand'].astype(str)\n",
    "    df['bodyType'] = df['bodyType'].astype(str)\n",
    "    df['fuelType'] = df['fuelType'].astype(str)\n",
    "    df['gearbox'] = df['gearbox'].astype(str)\n",
    "    df['notRepairedDamage'] = df['notRepairedDamage'].astype(str)\n",
    "    df['regionCode'] = df['regionCode'].astype(int).astype(str)\n",
    "    df['seller'] = df['seller'].astype(int).astype(str)\n",
    "    df['offerType'] = df['offerType'].astype(int).astype(str)\n",
    "    df['regDate'] = df['regDate'].astype(str)\n",
    "    df['creatDate'] = df['creatDate'].astype(str)\n",
    "    \n",
    "    # date\n",
    "    df['creatDate'] = pd.to_datetime(df['creatDate'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = data_astype(all_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编码分类变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先删除掉一些不要的特征\n",
    "all_features = all_features.drop(['SaleID', 'name', 'regDate', 'model', 'seller',\n",
    "                                  'offerType', 'creatDate', 'regionCode'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199766, 77)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features = pd.get_dummies(all_features).reset_index(drop=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重新创建训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((149766, 77), (149766,), (50000, 77))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = all_features.iloc[:len(train_labels), :]\n",
    "X_test = all_features.iloc[len(train_labels):, :]\n",
    "X.shape, train_labels.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge, RidgeCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K折交叉验证\n",
    "kf = KFold(n_splits=5, random_state=15, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义评测指标\n",
    "def cv_mae(model, X):\n",
    "    mae = -cross_val_score(model, X, train_labels, scoring='neg_mean_absolute_error')\n",
    "    return mae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_alphas = [0.1, 1, 3, 5, 10]\n",
    "ridge = RidgeCV(alphas=ridge_alphas, cv=kf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看交叉验证分数\n",
    "score = cv_mae(ridge, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19779685, 0.19911369, 0.19930195, 0.19632974, 0.20047872])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19860419017501124"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 0.1,  1. ,  3. ,  5. , 10. ]),\n",
       "        cv=KFold(n_splits=5, random_state=15, shuffle=True), fit_intercept=True,\n",
       "        gcv_mode=None, normalize=False, scoring=None, store_cv_values=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9368702755273388"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看R的平方\n",
    "ridge.score(X, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb43e8e3c8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看预测结果\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "sns.distplot(ridge.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测结果\n",
    "submission = test[['SaleID']].copy()\n",
    "submission['price'] = np.expm1(ridge.predict(X_test))\n",
    "submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "384px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
